1. Field of the Invention
The present invention is directed to the analysis of radiographs using artificial neural networks which classify the radiographs into normal and abnormal.
2. Discussion of the Related Art
Interstitial lung disease is one of the most common findings in abnormal chest radiographs, as is described by H. MacMahon, K J M. Liu, S. M. Montner, and K. Doi, in "The nature and subtlety of abnormal findings in chest radiographs," Med. Phys. 18: 206-210 (1991). However, due to the subjectivity in the radiologists' interpretation of interstitial disease, the diagnosis of interstitial lung disease is considered a difficult task for radiologists because no quantitative criteria exists for distinction between normal patterns and subtle abnormal infiltrate patterns on chest radiographs. This subjectivity is reported in "Disagreement in chest Roentgen interpretation," Chest 68, 278-282 (1975) by P. G. Herman, D. E. Gerson, S. J. Hessel, B. S. Mayer, M. Watnick, B. Blesser and D. Ozonoff. Therefore, Applicants have been developing computer-aided diagnosis (CAD) schemes for quantitative analysis of interstitial infiltrates to improve diagnostic accuracy and reproducibility. Two different CAD schemes for detection and characterization of interstitial lung disease are disclosed in "Image feature analysis and computer-aided diagnosis in digital radiography: Detection and characterization of interstitial lung diseases in digital chest radiographs," Med. Phys. 15: 311-319 (1988) by S. Katsuragawa, K. Doi and H. MacMahon; "Image feature analysis and computer-aided diagnosis in digital radiography: Classification of normal and abnormal lungs with interstitial diseases in chest images," Med. Phys. 16: 38-44 (1989) by S. Katsuragawa, K. Doi and H. MacMahon; "Automated selection of regions of interest for quantitative analysis of lung textures in digital chest radiographs," Med. Phys. 20, 975-982 (1993) by X. Chen, K. Doi, S. Katsuragawa and H. MacMahon; "Computer-aided diagnosis for interstitial infiltrates in chest radiographs: Optical-density dependence of texture measures," Med. Phys. 22: 1515-1522 (1995) by J. Morishita, K. Doi, S. Katsuragawa, L. Monnier-Cholley and H. MacMahon; "Computerized analysis of interstitial infiltrates on chest radiographs: A new scheme based on geometric-pattern features and Fourier analysis," Acad. Radiol. 2, 455-462 (1995) by L. Monnier-Cholley, H. Macmahon, S. Katuragawa, J. Morishita and K. Doi; and "Quantitative analysis of geometric-pattern features of interstitial infiltrates in digital chest radiographs: Preliminary results," Journal of Digital Imaging 9, 137-144 (1996) by S. Katsuragawa, K. Doi, H. MacMahon, L. Monnier-Cholley, J. Morishita and T. Ishida, each of which is incorporated herein by reference. These schemes include texture analysis by use of Fourier transform and geometric-pattern analysis and extract image features associated with interstitial infiltrate patterns from digitized chest radiographs. These features are the RMS variation and the first moment of the power spectrum obtained by texture analysis which correspond to the magnitude and the coarseness (or fineness) of the infiltrates. In addition, by geometric-pattern analysis, the total area of the area components and the total length of the line components are obtained, which are related to the nodular opacity and linear opacity, respectively, in interstitial infiltrate patterns. Some useful information related to interstitial infiltrate patterns can be extracted by these schemes. Although the performance of these schemes is generally very good, there are still some cases in which normal and abnormal cases are not correctly determined. An example of the misclassification is reported in "Computer-aided diagnosis in chest radiography: Preliminary experience," Invest. Radio. 28, 987-993 (1993), by K. Abe, K. Doi, H. MacMahon, M. L. Giger, H. Hia, X. Chen, A. Kano, and T. Yanagisawa, incorporated herein by reference. Therefore, it is useful to develop an alternative approach based on image data in lung fields of chest radiographs.
Artificial neural networks (ANN) have been applied to several chest CAD schemes. "Potential usefulness of an artificial neural network for differential diagnosis of interstitial diseases: Pilot study," Radiology 177, 857-860 (1990) by N. Asada, K. Doi, H. MacMahon, S. M. Montner, M. L. Giger, C. Abe, and Y. Wu, examines differential diagnosis of interstitial lung diseases on the basis of clinical and radiographic information, whereas "Computerized analysis for automated detection of lung nodules in digitized chest radiographs," Ph.D. dissertation of the University of Chicago (1996), by X. W. Xu examines the elimination of false positives in the detection of lung nodules. ANNs used in these schemes were trained with some extracted features associated with lesions and/or false positives.